{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "import pickle\n",
    "import itertools\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.metrics import label_ranking_average_precision_score, label_ranking_loss, coverage_error \n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "from scipy.signal import resample\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "import pickle\n",
    "\n",
    "\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, Conv1D, MaxPooling1D, Softmax, Add, Flatten, Activation# , Dropout\n",
    "from keras import backend as K\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"D:/cao/kaggle/ECG/mitbih_train.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87554 entries, 0 to 87553\n",
      "Columns: 188 entries, 0 to 187\n",
      "dtypes: float64(188)\n",
      "memory usage: 125.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"D:/cao/kaggle/ECG/mitbih_test.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21892 entries, 0 to 21891\n",
      "Columns: 188 entries, 0 to 187\n",
      "dtypes: float64(188)\n",
      "memory usage: 31.4 MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练集和测试集合并在一张表里\n",
    "\n",
    "df = pd.concat([df, df2], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 109446 entries, 0 to 21891\n",
      "Columns: 188 entries, 0 to 187\n",
      "dtypes: float64(188)\n",
      "memory usage: 157.8 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    90589\n",
       "4.0     8039\n",
       "2.0     7236\n",
       "1.0     2779\n",
       "3.0      803\n",
       "Name: 187, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看 5 个类的样本数\n",
    "\n",
    "df[187].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = df.values\n",
    "X = M[:, :-1]\n",
    "y = M[:, -1].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109446, 187)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109446,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除不再使用的变量\n",
    "\n",
    "del df\n",
    "del df2\n",
    "del M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制 5 种不同类别的心电图\n",
    "\n",
    "C0 = np.argwhere(y == 0).flatten()\n",
    "C1 = np.argwhere(y == 1).flatten()\n",
    "C2 = np.argwhere(y == 2).flatten()\n",
    "C3 = np.argwhere(y == 3).flatten()\n",
    "C4 = np.argwhere(y == 4).flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在实验中，只使用 ECG 导联 II，MI 及健康的样本。且在实验中，重新的采样频率为 125 Hz。即为 0.008s。\n",
    "\n",
    "一个信号包含 187 个值，大约有 1.5s。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sb\n",
    "import scipy as sc\n",
    "sb.set_style(\"whitegrid\") # 显示网格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 取每个类别的其中一组数据来展示\n",
    "\n",
    "# 限定 x 的范围，即限定时间的范围\n",
    "x = np.arange(0, 187)*0.008\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(x, X[C0, :][0], label='Cat. N')\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"ECG for C0\", fontsize=15)\n",
    "plt.ylabel(\"Amplitude\", fontsize=10)\n",
    "plt.xlabel(\"Time (s)\", fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 187)*0.008\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(x, X[C1, :][0], label='Cat. S')\n",
    "plt.legend()\n",
    "plt.title(\"ECG for C1\", fontsize=15)\n",
    "plt.ylabel(\"Amplitude\", fontsize=10)\n",
    "plt.xlabel(\"Time (s)\", fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 187)*0.008\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(x, X[C2, :][0], label='Cat. V')\n",
    "plt.legend()\n",
    "plt.title(\"ECG for C2\", fontsize=15)\n",
    "plt.ylabel(\"Amplitude\", fontsize=10)\n",
    "plt.xlabel(\"Time (s)\", fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 187)*0.008\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(x, X[C3, :][0], label='Cat. F')\n",
    "plt.legend()\n",
    "plt.title(\"ECG for C3\", fontsize=15)\n",
    "plt.ylabel(\"Amplitude\", fontsize=10)\n",
    "plt.xlabel(\"Time (s)\", fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 187)*0.008\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "#plt.plot(x, X[C0, :][0], label='Cat. N')\n",
    "#plt.plot(x, X[C1, :][0], label='Cat. S')\n",
    "#plt.plot(x, X[C2, :][0], label='Cat. V')\n",
    "#plt.plot(x, X[C3, :][0], label='Cat. F')\n",
    "plt.plot(x, X[C4, :][0], label='Cat. Q')\n",
    "plt.legend()\n",
    "plt.title(\"ECG for C4\", fontsize=15)\n",
    "plt.ylabel(\"Amplitude\", fontsize=10)\n",
    "plt.xlabel(\"Time (s)\", fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 187)*0.008\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(x, X[C0, :][0], label='Cat. N')\n",
    "plt.plot(x, X[C1, :][0], label='Cat. S')\n",
    "plt.plot(x, X[C2, :][0], label='Cat. V')\n",
    "plt.plot(x, X[C3, :][0], label='Cat. F')\n",
    "plt.plot(x, X[C4, :][0], label='Cat. Q')\n",
    "plt.legend()\n",
    "plt.title(\"1-beat ECG for every category\", fontsize=15)\n",
    "plt.ylabel(\"Amplitude\", fontsize=10)\n",
    "plt.xlabel(\"Time (s)\", fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据增强\n",
    "\n",
    "为了使所有类别的数据都处于相同的水平，所以需要进行数据增强处理。然而，这里只是将最少的类（第 3 类）增加到与类别 1 相同的级别。由此我们将能够具有大约 5x800 观测值的测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 伸展\n",
    "def stretch(x):\n",
    "    # 将 187 的长度随机重新采样\n",
    "    l = int(187 * (1 + (random.random()-0.5)/3))\n",
    "    y = resample(x, l)\n",
    "    # 长度小于 187 的填充 0\n",
    "    if l < 187:\n",
    "        y_ = np.zeros(shape=(187,))\n",
    "        y_[:l] = y\n",
    "    # 长度大于 187 的裁剪到 187 的长度\n",
    "    else:\n",
    "        y_ = y[:187]\n",
    "    return y_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x209096dfa20>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.plot(x, X[0, :], label='original signal') # X[0, :] == X[0]\n",
    "ax.plot(x, stretch(X[0, :]), label='stretch signal')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amplifie le signal suivant la formule suivante :\n",
    "\n",
    "new_x =  $-\\alpha\\cdot x^2 + -\\alpha\\cdot x^2 + x$ avec $-0.5﹤alpha﹤0.5$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 放大\n",
    "def amplify(x):\n",
    "    alpha = (random.random()-0.5)\n",
    "    factor = -alpha*x + (1+alpha) # 放大的幅度几乎很小\n",
    "    return x*factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x209086213c8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.plot(x, X[0, :], label='original signal') # X[0, :] == X[0]\n",
    "ax.plot(x, amplify(X[0, :]), label='amplify signal')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def augment(x):\n",
    "    \n",
    "    result = np.zeros(shape=(4, 187))\n",
    "    for i in range(3):\n",
    "        if random.random() < 0.33:\n",
    "            new_y = stretch(x)\n",
    "        elif random.random() < 0.66:\n",
    "            new_y = amplify(x)\n",
    "        else:\n",
    "            new_y = stretch(x)\n",
    "            new_y = amplify(new_y)\n",
    "        result[i, :] = new_y\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将 augment 这个函数按照所设定的 axis 应用\n",
    "result = np.apply_along_axis(augment, axis=1, arr=X[C3]).reshape(-1, 187)\n",
    "\n",
    "# 将 C3 的数据增强到与其他一样的级别\n",
    "classes = np.ones(shape=(result.shape[0],),dtype=int)*3\n",
    "\n",
    "# 按垂直方向堆叠扩增的数据\n",
    "X = np.vstack([X, result]) \n",
    "y = np.hstack([y, classes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 随机从各个类别中抽取 800 个索引，用于后面建立测试集\n",
    "\n",
    "subC0 = np.random.choice(C0, 800)\n",
    "subC1 = np.random.choice(C1, 800)\n",
    "subC2 = np.random.choice(C2, 800)\n",
    "subC3 = np.random.choice(C3, 800)\n",
    "subC4 = np.random.choice(C4, 800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test set shape:  (4000, 187)\n",
      "test examples:  4000\n"
     ]
    }
   ],
   "source": [
    "X_test = np.vstack([X[subC0], X[subC1], X[subC2], X[subC3], X[subC4]])\n",
    "y_test = np.hstack([y[subC0], y[subC1], y[subC2], y[subC3], y[subC4]])\n",
    "print(\"test set shape: \", X_test.shape)\n",
    "print(\"test examples: \", X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train examples:  112362\n"
     ]
    }
   ],
   "source": [
    "# 除去测试集的数据，剩下的都是训练集\n",
    "\n",
    "X_train = np.delete(X, [subC0, subC1, subC2, subC3, subC4], axis=0)\n",
    "y_train = np.delete(y, [subC0, subC1, subC2, subC3, subC4], axis=0)\n",
    "print(\"train examples: \", y_train.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = shuffle(X_train, y_train, random_state=0)\n",
    "X_test, y_test = shuffle(X_test, y_test, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (112362, 187, 1)\n",
      "y_train (112362,)\n",
      "X_test (4000, 187, 1)\n",
      "y_test (4000,)\n"
     ]
    }
   ],
   "source": [
    "X_train = np.expand_dims(X_train, 2)\n",
    "X_test = np.expand_dims(X_test, 2)\n",
    "\n",
    "print(\"X_train\", X_train.shape)\n",
    "print(\"y_train\", y_train.shape)\n",
    "print(\"X_test\", X_test.shape)\n",
    "print(\"y_test\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\envs\\python36\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:368: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# 转换为 one-hot 向量\n",
    "\n",
    "ohe = OneHotEncoder()\n",
    "y_train = ohe.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ohe.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (112362, 187, 1)\n",
      "y_train (112362, 5)\n",
      "X_test (4000, 187, 1)\n",
      "y_test (4000, 5)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train\", X_train.shape)\n",
    "print(\"y_train\", y_train.shape)\n",
    "print(\"X_test\", X_test.shape)\n",
    "print(\"y_test\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![论文提出的模型结构](https://ai2-s2-public.s3-us-west-2.amazonaws.com/figures/2017-08-08/0efe20fd1dc65030fa57f7c0727f13b93d51adce/1-Figure1-1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_obs, feature, depth = X_train.shape\n",
    "batch_size = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "K.clear_session()\n",
    "\n",
    "def get_model():\n",
    "    \n",
    "    # Input\n",
    "    inp = Input(shape=(feature, depth))\n",
    "    # Conv\n",
    "    C = Conv1D(filters=32, kernel_size=5, strides=1)(inp)\n",
    "\n",
    "    # 【1】\n",
    "    # Conv\n",
    "    C11 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(C)\n",
    "    # ReLU\n",
    "    A11 = Activation(\"relu\")(C11)\n",
    "    # Conv\n",
    "    C12 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(A11)\n",
    "    # Shortcut\n",
    "    S11 = Add()([C12, C]) \n",
    "    # ReLU\n",
    "    A12 = Activation(\"relu\")(S11)\n",
    "    # Pool\n",
    "    M11 = MaxPooling1D(pool_size=5, strides=2)(A12)\n",
    "\n",
    "    #【2】\n",
    "    # Conv\n",
    "    C21 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(M11)\n",
    "    # ReLU\n",
    "    A21 = Activation(\"relu\")(C21)\n",
    "    # Conv\n",
    "    C22 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(A21)\n",
    "    # Shortcut\n",
    "    S21 = Add()([C22, M11])\n",
    "    # ReLU\n",
    "    A22 = Activation(\"relu\")(S11)\n",
    "    # Pool\n",
    "    M21 = MaxPooling1D(pool_size=5, strides=2)(A22)\n",
    "\n",
    "\n",
    "    # 【3】\n",
    "    # Conv\n",
    "    C31 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(M21)\n",
    "    # ReLU\n",
    "    A31 = Activation(\"relu\")(C31)\n",
    "    # Conv\n",
    "    C32 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(A31)\n",
    "    # Shortcut\n",
    "    S31 = Add()([C32, M21])\n",
    "    # ReLU\n",
    "    A32 = Activation(\"relu\")(S31)\n",
    "    # Pool\n",
    "    M31 = MaxPooling1D(pool_size=5, strides=2)(A32)\n",
    "\n",
    "    # 【4】\n",
    "    # Conv\n",
    "    C41 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(M31)\n",
    "    # ReLU\n",
    "    A41 = Activation(\"relu\")(C41)\n",
    "    # Conv\n",
    "    C42 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(A41)\n",
    "    # Shortcut\n",
    "    S41 = Add()([C42, M31])\n",
    "    # ReLU\n",
    "    A42 = Activation(\"relu\")(S41)\n",
    "    # Pool\n",
    "    M41 = MaxPooling1D(pool_size=5, strides=2)(A42)\n",
    "\n",
    "    # 【5】\n",
    "    # Conv\n",
    "    C51 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(M41)\n",
    "    # ReLU\n",
    "    A51 = Activation(\"relu\")(C51)\n",
    "    # Conv\n",
    "    C52 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same')(A51)\n",
    "    # Shortcut\n",
    "    S51 = Add()([C52, M41])\n",
    "    # ReLU\n",
    "    A52 = Activation(\"relu\")(S51)\n",
    "    # Pool\n",
    "    M51 = MaxPooling1D(pool_size=5, strides=2)(A52)\n",
    "\n",
    "    F1 = Flatten()(M51)\n",
    "\n",
    "    # FC\n",
    "    D1 = Dense(32, name='dense_1')(F1)\n",
    "    # ReLU\n",
    "    A6 = Activation(\"relu\")(D1)\n",
    "    # FC\n",
    "    D2 = Dense(32, name ='dense_2')(A6)\n",
    "    D3 = Dense(5, name='dense_3_mitdb')(D2)\n",
    "    # Softmax\n",
    "    A7 = Softmax()(D3)\n",
    "\n",
    "    model = Model(inputs=inp, outputs=A7)\n",
    "\n",
    "    model.summary()\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练心律失常分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 187, 1)       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 183, 32)      192         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 183, 32)      5152        conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 183, 32)      0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 183, 32)      5152        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 183, 32)      0           conv1d_3[0][0]                   \n",
      "                                                                 conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 183, 32)      0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, 90, 32)       0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 90, 32)       5152        max_pooling1d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 90, 32)       0           conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 90, 32)       5152        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 90, 32)       0           conv1d_7[0][0]                   \n",
      "                                                                 max_pooling1d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 90, 32)       0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 43, 32)       0           activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, 43, 32)       5152        max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 43, 32)       0           conv1d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_9 (Conv1D)               (None, 43, 32)       5152        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 43, 32)       0           conv1d_9[0][0]                   \n",
      "                                                                 max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 43, 32)       0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 20, 32)       0           activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_10 (Conv1D)              (None, 20, 32)       5152        max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 20, 32)       0           conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_11 (Conv1D)              (None, 20, 32)       5152        activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 20, 32)       0           conv1d_11[0][0]                  \n",
      "                                                                 max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 20, 32)       0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1D)  (None, 8, 32)        0           activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 256)          0           max_pooling1d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 32)           8224        flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 32)           0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 32)           1056        activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_3_mitdb (Dense)           (None, 5)            165         dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "softmax_1 (Softmax)             (None, 5)            0           dense_3_mitdb[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 50,853\n",
      "Trainable params: 50,853\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = get_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exp_decay(epoch):\n",
    "    initial_lrate = 0.001\n",
    "    k = 0.75\n",
    "    t = n_obs//(10000 * batch_size)  # every epoch we do n_obs/batch_size iteration\n",
    "    lrate = initial_lrate * math.exp(-k*t)\n",
    "    return lrate\n",
    "\n",
    "lrate = LearningRateScheduler(exp_decay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam = Adam(lr = 0.001, beta_1 = 0.9, beta_2 = 0.999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "file_path = 'ECG_mitbd.h5'\n",
    "checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 112362 samples, validate on 4000 samples\n",
      "Epoch 1/75\n",
      "112362/112362 [==============================] - 10s 92us/step - loss: 0.4097 - acc: 0.8712 - val_loss: 0.7340 - val_acc: 0.7765\n",
      "Epoch 2/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.1540 - acc: 0.9582 - val_loss: 0.5341 - val_acc: 0.8445\n",
      "Epoch 3/75\n",
      "112362/112362 [==============================] - 6s 54us/step - loss: 0.1122 - acc: 0.9688 - val_loss: 0.4454 - val_acc: 0.8635\n",
      "Epoch 4/75\n",
      "112362/112362 [==============================] - 6s 54us/step - loss: 0.0907 - acc: 0.9741 - val_loss: 0.3231 - val_acc: 0.8955\n",
      "Epoch 5/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0775 - acc: 0.9773 - val_loss: 0.3957 - val_acc: 0.8872\n",
      "Epoch 6/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0680 - acc: 0.9795 - val_loss: 0.2841 - val_acc: 0.9100\n",
      "Epoch 7/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0618 - acc: 0.9812 - val_loss: 0.2591 - val_acc: 0.9040\n",
      "Epoch 8/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0577 - acc: 0.9820 - val_loss: 0.3290 - val_acc: 0.9080\n",
      "Epoch 9/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0502 - acc: 0.9843 - val_loss: 0.4034 - val_acc: 0.8880\n",
      "Epoch 10/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0480 - acc: 0.9849 - val_loss: 0.2423 - val_acc: 0.9210\n",
      "Epoch 11/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0443 - acc: 0.9859 - val_loss: 0.2815 - val_acc: 0.9143\n",
      "Epoch 12/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0408 - acc: 0.9867 - val_loss: 0.1765 - val_acc: 0.9450\n",
      "Epoch 13/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0389 - acc: 0.9875 - val_loss: 0.3872 - val_acc: 0.8985\n",
      "Epoch 14/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0355 - acc: 0.9886 - val_loss: 0.1788 - val_acc: 0.9460\n",
      "Epoch 15/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0335 - acc: 0.9891 - val_loss: 0.2158 - val_acc: 0.9355\n",
      "Epoch 16/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0324 - acc: 0.9891 - val_loss: 0.2596 - val_acc: 0.9223\n",
      "Epoch 17/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0293 - acc: 0.9901 - val_loss: 0.2160 - val_acc: 0.9342\n",
      "Epoch 18/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0285 - acc: 0.9905 - val_loss: 0.2262 - val_acc: 0.9373\n",
      "Epoch 19/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0291 - acc: 0.9904 - val_loss: 0.1706 - val_acc: 0.9485\n",
      "Epoch 20/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0271 - acc: 0.9906 - val_loss: 0.2320 - val_acc: 0.9373\n",
      "Epoch 21/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0263 - acc: 0.9914 - val_loss: 0.2631 - val_acc: 0.9272\n",
      "Epoch 22/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0263 - acc: 0.9914 - val_loss: 0.2628 - val_acc: 0.9360\n",
      "Epoch 23/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0245 - acc: 0.9917 - val_loss: 0.2653 - val_acc: 0.9360\n",
      "Epoch 24/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0231 - acc: 0.9920 - val_loss: 0.2712 - val_acc: 0.9290\n",
      "Epoch 25/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0227 - acc: 0.9923 - val_loss: 0.2965 - val_acc: 0.9318\n",
      "Epoch 26/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0203 - acc: 0.9931 - val_loss: 0.2441 - val_acc: 0.9408\n",
      "Epoch 27/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0200 - acc: 0.9932 - val_loss: 0.2054 - val_acc: 0.9502\n",
      "Epoch 28/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0201 - acc: 0.9931 - val_loss: 0.2760 - val_acc: 0.9300\n",
      "Epoch 29/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0199 - acc: 0.9931 - val_loss: 0.2056 - val_acc: 0.9473\n",
      "Epoch 30/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0189 - acc: 0.9934 - val_loss: 0.1851 - val_acc: 0.9525\n",
      "Epoch 31/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0175 - acc: 0.9940 - val_loss: 0.2813 - val_acc: 0.9412\n",
      "Epoch 32/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0172 - acc: 0.9940 - val_loss: 0.2397 - val_acc: 0.9455\n",
      "Epoch 33/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0145 - acc: 0.9949 - val_loss: 0.2532 - val_acc: 0.9460\n",
      "Epoch 34/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0173 - acc: 0.9937 - val_loss: 0.2403 - val_acc: 0.9402\n",
      "Epoch 35/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0170 - acc: 0.9939 - val_loss: 0.2230 - val_acc: 0.9375\n",
      "Epoch 36/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0132 - acc: 0.9955 - val_loss: 0.2613 - val_acc: 0.9397\n",
      "Epoch 37/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0165 - acc: 0.9942 - val_loss: 0.3544 - val_acc: 0.9270\n",
      "Epoch 38/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0162 - acc: 0.9943 - val_loss: 0.2347 - val_acc: 0.9497\n",
      "Epoch 39/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0164 - acc: 0.9943 - val_loss: 0.2230 - val_acc: 0.9553\n",
      "Epoch 40/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0120 - acc: 0.9958 - val_loss: 0.3016 - val_acc: 0.9293\n",
      "Epoch 41/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0136 - acc: 0.9952 - val_loss: 0.2473 - val_acc: 0.9467\n",
      "Epoch 42/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0132 - acc: 0.9954 - val_loss: 0.2944 - val_acc: 0.9467\n",
      "Epoch 43/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0139 - acc: 0.9952 - val_loss: 0.2463 - val_acc: 0.9485\n",
      "Epoch 44/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0134 - acc: 0.9952 - val_loss: 0.3251 - val_acc: 0.9285\n",
      "Epoch 45/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0123 - acc: 0.9956 - val_loss: 0.2759 - val_acc: 0.9455\n",
      "Epoch 46/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0123 - acc: 0.9957 - val_loss: 0.2412 - val_acc: 0.9477\n",
      "Epoch 47/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0127 - acc: 0.9957 - val_loss: 0.2041 - val_acc: 0.9590\n",
      "Epoch 48/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0132 - acc: 0.9951 - val_loss: 0.2152 - val_acc: 0.9543\n",
      "Epoch 49/75\n",
      "112362/112362 [==============================] - 6s 50us/step - loss: 0.0116 - acc: 0.9959 - val_loss: 0.2359 - val_acc: 0.9518\n",
      "Epoch 50/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0102 - acc: 0.9965 - val_loss: 0.3707 - val_acc: 0.9395\n",
      "Epoch 51/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0118 - acc: 0.9958 - val_loss: 0.2569 - val_acc: 0.9470\n",
      "Epoch 52/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0114 - acc: 0.9963 - val_loss: 0.2599 - val_acc: 0.9460\n",
      "Epoch 53/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0113 - acc: 0.9961 - val_loss: 0.2866 - val_acc: 0.9382\n",
      "Epoch 54/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0096 - acc: 0.9965 - val_loss: 0.2731 - val_acc: 0.9475\n",
      "Epoch 55/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0088 - acc: 0.9968 - val_loss: 0.2776 - val_acc: 0.9500\n",
      "Epoch 56/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0126 - acc: 0.9954 - val_loss: 0.2795 - val_acc: 0.9463\n",
      "Epoch 57/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0114 - acc: 0.9960 - val_loss: 0.2665 - val_acc: 0.9542\n",
      "Epoch 58/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0099 - acc: 0.9966 - val_loss: 0.2882 - val_acc: 0.9453\n",
      "Epoch 59/75\n",
      "112362/112362 [==============================] - 6s 55us/step - loss: 0.0108 - acc: 0.9962 - val_loss: 0.3274 - val_acc: 0.9457\n",
      "Epoch 60/75\n",
      "112362/112362 [==============================] - 6s 55us/step - loss: 0.0122 - acc: 0.9956 - val_loss: 0.3385 - val_acc: 0.9295\n",
      "Epoch 61/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0083 - acc: 0.9970 - val_loss: 0.2976 - val_acc: 0.9483\n",
      "Epoch 62/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0107 - acc: 0.9963 - val_loss: 0.2904 - val_acc: 0.9530\n",
      "Epoch 63/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0087 - acc: 0.9971 - val_loss: 0.3461 - val_acc: 0.9343\n",
      "Epoch 64/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0097 - acc: 0.9967 - val_loss: 0.2387 - val_acc: 0.9487\n",
      "Epoch 65/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0104 - acc: 0.9963 - val_loss: 0.3618 - val_acc: 0.9380\n",
      "Epoch 66/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0094 - acc: 0.9966 - val_loss: 0.3251 - val_acc: 0.9330\n",
      "Epoch 67/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0125 - acc: 0.9954 - val_loss: 0.3828 - val_acc: 0.9415\n",
      "Epoch 68/75\n",
      "112362/112362 [==============================] - 6s 51us/step - loss: 0.0099 - acc: 0.9965 - val_loss: 0.3789 - val_acc: 0.9260\n",
      "Epoch 69/75\n",
      "112362/112362 [==============================] - 6s 53us/step - loss: 0.0110 - acc: 0.9963 - val_loss: 0.2504 - val_acc: 0.9542\n",
      "Epoch 70/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0071 - acc: 0.9975 - val_loss: 0.3687 - val_acc: 0.9350\n",
      "Epoch 71/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0067 - acc: 0.9977 - val_loss: 0.3490 - val_acc: 0.9425\n",
      "Epoch 72/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0102 - acc: 0.9963 - val_loss: 0.3125 - val_acc: 0.9565\n",
      "Epoch 73/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0062 - acc: 0.9979 - val_loss: 0.3368 - val_acc: 0.9485\n",
      "Epoch 74/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0091 - acc: 0.9968 - val_loss: 0.2687 - val_acc: 0.9500\n",
      "Epoch 75/75\n",
      "112362/112362 [==============================] - 6s 52us/step - loss: 0.0103 - acc: 0.9965 - val_loss: 0.3006 - val_acc: 0.9480\n"
     ]
    }
   ],
   "source": [
    "from livelossplot import PlotLossesKeras # for visualizing the loss and accuracy\n",
    "\n",
    "history = model.fit(X_train, y_train, \n",
    "                    epochs=75, \n",
    "                    batch_size=batch_size, \n",
    "                    verbose=1, \n",
    "                    validation_data=(X_test, y_test), \n",
    "                    callbacks=[lrate]) # callbacks=[PlotLossesKeras()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set_style(\"white\") # 不显示网格\n",
    "\n",
    "# 绘制准确率\n",
    "plt.plot(history.history['acc'])  \n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')  \n",
    "plt.ylabel('accuracy')  \n",
    "plt.xlabel('epoch')  \n",
    "plt.xlim(0, 80)\n",
    "plt.legend(['train', 'validation'], loc='lower right') \n",
    "plt.show()  \n",
    "\n",
    "# 绘制 loss \n",
    "plt.plot(history.history['loss'])  \n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')  \n",
    "plt.ylabel('loss')  \n",
    "plt.xlabel('epoch')  \n",
    "plt.xlim(0, 80)\n",
    "plt.legend(['train', 'validation'], loc='upper right')  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test, batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      1.00      0.91       800\n",
      "           1       1.00      0.85      0.92       800\n",
      "           2       0.96      0.95      0.96       800\n",
      "           3       0.98      0.98      0.98       800\n",
      "           4       1.00      0.96      0.98       800\n",
      "\n",
      "   micro avg       0.95      0.95      0.95      4000\n",
      "   macro avg       0.95      0.95      0.95      4000\n",
      "weighted avg       0.95      0.95      0.95      4000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test.argmax(axis=1), y_pred.argmax(axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ranking-based average precision: 0.972\n",
      "Rnaking loss: 0.016\n",
      "Coverge_error: 1.064\n"
     ]
    }
   ],
   "source": [
    "print(\"ranking-based average precision: {:.3f}\".format(label_ranking_average_precision_score(y_test.todense(), y_pred)))\n",
    "print(\"Rnaking loss: {:.3f}\".format(label_ranking_loss(y_test.todense(), y_pred)))\n",
    "print(\"Coverge_error: {:.3f}\".format(coverage_error(y_test.todense(), y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting 'normalize=True'.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "        \n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    \n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure(figsize=(10,10))\n",
    "plot_confusion_matrix(cnf_matrix, classes=['N', 'S', 'V', 'F', 'Q'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练 MI 预测器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "ptb_nor = pd.read_csv('ptbdb_normal.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4046 entries, 0 to 4045\n",
      "Columns: 188 entries, 0 to 187\n",
      "dtypes: float64(188)\n",
      "memory usage: 5.8 MB\n"
     ]
    }
   ],
   "source": [
    "ptb_nor.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.171923</td>\n",
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       "      <td>0.072732</td>\n",
       "      <td>0.055619</td>\n",
       "      <td>0.048774</td>\n",
       "      <td>0.054478</td>\n",
       "      <td>0.041643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.867238</td>\n",
       "      <td>0.201360</td>\n",
       "      <td>0.099349</td>\n",
       "      <td>0.141336</td>\n",
       "      <td>0.120934</td>\n",
       "      <td>0.108516</td>\n",
       "      <td>0.096393</td>\n",
       "      <td>0.093436</td>\n",
       "      <td>0.100828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 188 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "0  1.000000  0.900324  0.358590  0.051459  0.046596  0.126823  0.133306   \n",
       "1  1.000000  0.794681  0.375387  0.116883  0.000000  0.171923  0.283859   \n",
       "2  0.909029  0.791482  0.423169  0.186712  0.000000  0.007836  0.063032   \n",
       "3  1.000000  0.478893  0.056760  0.064176  0.081289  0.072732  0.055619   \n",
       "4  1.000000  0.867238  0.201360  0.099349  0.141336  0.120934  0.108516   \n",
       "\n",
       "        7         8         9   ...   178  179  180  181  182  183  184  185  \\\n",
       "0  0.119125  0.110616  0.113047 ...   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1  0.293754  0.325912  0.345083 ...   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  0.077002  0.074957  0.077342 ...   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  0.048774  0.054478  0.041643 ...   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4  0.096393  0.093436  0.100828 ...   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "   186  187  \n",
       "0  0.0  0.0  \n",
       "1  0.0  0.0  \n",
       "2  0.0  0.0  \n",
       "3  0.0  0.0  \n",
       "4  0.0  0.0  \n",
       "\n",
       "[5 rows x 188 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptb_nor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "ptb_abnor = pd.read_csv('ptbdb_abnormal.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10506 entries, 0 to 10505\n",
      "Columns: 188 entries, 0 to 187\n",
      "dtypes: float64(188)\n",
      "memory usage: 15.1 MB\n"
     ]
    }
   ],
   "source": [
    "ptb_abnor.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 188 columns</p>\n",
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      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
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       "1  1.000000  0.606941  0.384181  0.254237  0.223567  0.276836  0.253430   \n",
       "2  1.000000  0.951613  0.923963  0.853303  0.791859  0.734255  0.672043   \n",
       "3  0.977819  0.899261  0.230129  0.032348  0.142329  0.223660  0.328096   \n",
       "4  0.935618  0.801661  0.805815  1.000000  0.722741  0.480789  0.454829   \n",
       "\n",
       "        7         8         9   ...   178  179  180  181  182  183  184  185  \\\n",
       "0  0.749783  0.687229  0.635100 ...   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1  0.184826  0.153349  0.121872 ...   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  0.685100  0.670507  0.667435 ...   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  0.367837  0.381701  0.389094 ...   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4  0.319834  0.266874  0.308411 ...   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "   186  187  \n",
       "0  0.0  1.0  \n",
       "1  0.0  1.0  \n",
       "2  0.0  1.0  \n",
       "3  0.0  1.0  \n",
       "4  0.0  1.0  \n",
       "\n",
       "[5 rows x 188 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptb_abnor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([ptb_nor, ptb_abnor], sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=1337, stratify=df[187])\n",
    "\n",
    "\n",
    "Y = np.array(df_train[187].values).astype(np.int8)\n",
    "X = np.array(df_train[list(range(187))].values)[..., np.newaxis]\n",
    "\n",
    "Y_test = np.array(df_test[187].values).astype(np.int8)\n",
    "X_test = np.array(df_test[list(range(187))].values)[..., np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_obs, feature, depth = X.shape\n",
    "batch_size = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "K.clear_session()\n",
    "\n",
    "def MI_model():\n",
    "    \n",
    "    # Input\n",
    "    inp = Input(shape=(feature, depth))\n",
    "    # Conv\n",
    "    C = Conv1D(filters=32, kernel_size=5, strides=1, trainable=False)(inp)\n",
    "\n",
    "    # 【1】\n",
    "    # Conv\n",
    "    C11 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same', trainable=False)(C)\n",
    "    # ReLU\n",
    "    A11 = Activation(\"relu\")(C11)\n",
    "    # Conv\n",
    "    C12 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same', trainable=False)(A11)\n",
    "    # Shortcut\n",
    "    S11 = Add()([C12, C]) \n",
    "    # ReLU\n",
    "    A12 = Activation(\"relu\")(S11)\n",
    "    # Pool\n",
    "    M11 = MaxPooling1D(pool_size=5, strides=2)(A12)\n",
    "\n",
    "    #【2】\n",
    "    # Conv\n",
    "    C21 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same', trainable=False)(M11)\n",
    "    # ReLU\n",
    "    A21 = Activation(\"relu\")(C21)\n",
    "    # Conv\n",
    "    C22 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same', trainable=False)(A21)\n",
    "    # Shortcut\n",
    "    S21 = Add()([C22, M11])\n",
    "    # ReLU\n",
    "    A22 = Activation(\"relu\")(S11)\n",
    "    # Pool\n",
    "    M21 = MaxPooling1D(pool_size=5, strides=2)(A22)\n",
    "\n",
    "\n",
    "    # 【3】\n",
    "    # Conv\n",
    "    C31 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same', trainable=False)(M21)\n",
    "    # ReLU\n",
    "    A31 = Activation(\"relu\")(C31)\n",
    "    # Conv\n",
    "    C32 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same', trainable=False)(A31)\n",
    "    # Shortcut\n",
    "    S31 = Add()([C32, M21])\n",
    "    # ReLU\n",
    "    A32 = Activation(\"relu\")(S31)\n",
    "    # Pool\n",
    "    M31 = MaxPooling1D(pool_size=5, strides=2)(A32)\n",
    "\n",
    "    # 【4】\n",
    "    # Conv\n",
    "    C41 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same', trainable=False)(M31)\n",
    "    # ReLU\n",
    "    A41 = Activation(\"relu\")(C41)\n",
    "    # Conv\n",
    "    C42 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same', trainable=False)(A41)\n",
    "    # Shortcut\n",
    "    S41 = Add()([C42, M31])\n",
    "    # ReLU\n",
    "    A42 = Activation(\"relu\")(S41)\n",
    "    # Pool\n",
    "    M41 = MaxPooling1D(pool_size=5, strides=2)(A42)\n",
    "\n",
    "    # 【5】\n",
    "    # Conv\n",
    "    C51 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same', trainable=False)(M41)\n",
    "    # ReLU\n",
    "    A51 = Activation(\"relu\")(C51)\n",
    "    # Conv\n",
    "    C52 = Conv1D(filters=32, kernel_size=5, strides=1, padding='same', trainable=False)(A51)\n",
    "    # Shortcut\n",
    "    S51 = Add()([C52, M41])\n",
    "    # ReLU\n",
    "    A52 = Activation(\"relu\")(S51)\n",
    "    # Pool\n",
    "    M51 = MaxPooling1D(pool_size=5, strides=2)(A52)\n",
    "\n",
    "    F1 = Flatten()(M51)\n",
    "\n",
    "    # FC\n",
    "    D1 = Dense(32, name='dense_1')(F1)\n",
    "    # ReLU\n",
    "    A6 = Activation(\"relu\")(D1)\n",
    "    # FC\n",
    "    D2 = Dense(32, name='dense_2')(A6)\n",
    "    D3 = Dense(1, activation='sigmoid', name='dense_3_ptbdb')(D2)\n",
    "\n",
    "    model = Model(inputs=inp, outputs=D3)\n",
    "\n",
    "    model.summary()\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 187, 1)       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 183, 32)      192         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 183, 32)      5152        conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 183, 32)      0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 183, 32)      5152        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 183, 32)      0           conv1d_3[0][0]                   \n",
      "                                                                 conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 183, 32)      0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, 90, 32)       0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 90, 32)       5152        max_pooling1d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 90, 32)       0           conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 90, 32)       5152        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 90, 32)       0           conv1d_7[0][0]                   \n",
      "                                                                 max_pooling1d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 90, 32)       0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 43, 32)       0           activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, 43, 32)       5152        max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 43, 32)       0           conv1d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_9 (Conv1D)               (None, 43, 32)       5152        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 43, 32)       0           conv1d_9[0][0]                   \n",
      "                                                                 max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 43, 32)       0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 20, 32)       0           activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_10 (Conv1D)              (None, 20, 32)       5152        max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 20, 32)       0           conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_11 (Conv1D)              (None, 20, 32)       5152        activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 20, 32)       0           conv1d_11[0][0]                  \n",
      "                                                                 max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 20, 32)       0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1D)  (None, 8, 32)        0           activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 256)          0           max_pooling1d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 32)           8224        flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 32)           0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 32)           1056        activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_3_ptbdb (Dense)           (None, 1)            33          dense_2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 50,721\n",
      "Trainable params: 9,313\n",
      "Non-trainable params: 41,408\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = MI_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam = Adam(lr = 0.001, beta_1 = 0.9, beta_2 = 0.999)\n",
    "model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'ECG_ptbdb.h5'\n",
    "\n",
    "checkout = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
    "early = EarlyStopping(monitor=\"val_acc\", mode=\"max\", patience=5, verbose=1)\n",
    "#redonplat = ReduceLROnPlateau(monitor=\"val_acc\", mode=\"max\", patience=3, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('ECG_mitdb.h5', by_name=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log-loss (cost function):\n",
      "training   (min:    0.075, max:    0.082, cur:    0.079)\n",
      "validation (min:    0.113, max:    0.125, cur:    0.119)\n",
      "\n",
      "Accuracy:\n",
      "training   (min:    0.970, max:    0.974, cur:    0.970)\n",
      "validation (min:    0.953, max:    0.961, cur:    0.957)\n",
      "Epoch 00010: early stopping\n"
     ]
    }
   ],
   "source": [
    "from livelossplot import PlotLossesKeras # for visualizing the loss and accuracy\n",
    "\n",
    "history = model.fit(X, Y, \n",
    "                    epochs=100, \n",
    "                    batch_size=batch_size, \n",
    "                    verbose=0, \n",
    "                    validation_data=(X_test, Y_test), \n",
    "                    callbacks=[checkout, PlotLossesKeras(), early]) # callbacks=[PlotLossesKeras()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model.predict(X_test, batch_size=512).reshape(Y_test.shape[0],)\n",
    "Y_pred = (Y_pred>0.5).astype(np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy score : 0.9574029543112332 \n"
     ]
    }
   ],
   "source": [
    "acc = accuracy_score(Y_test, Y_pred)\n",
    "\n",
    "print(\"Test accuracy score : %s \"% acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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